code: def greedy_decode(
      self,
      data: List[CodeGraph2Seq],
      trained_network: Graph2SeqModule,
      device: Any,
  ) -> List[Tuple[List[str], float]]:
      decoded_sequences = []
      for mb_data, input_data in self.minibatch_iterator(
          self.tensorize_dataset(iter(data), return_input_data=True),
          device,
          max_minibatch_size=50,
      ):
          input_concrete_values: List[str] = []
          for sample in input_data:
              sample = enforce_not_None(sample)
              input_concrete_values.extend(
                  sample["node_labels"][k].lower()
                  for k in sample["backbone_sequence"]
              )
  
          with torch.no_grad():
              gnn_output = trained_network._gnn(
                  **mb_data["encoder_mb_data"]
              )  # type: GnnOutput
              mb_outputs = self.__decoder_model.greedy_decode(
                  input_concrete_values=input_concrete_values,
                  input_memories=gnn_output.output_node_representations[
                      gnn_output.node_idx_references["backbone_nodes"]
                  ],
                  input_memories_origin_idx=gnn_output.node_graph_idx_reference[
                      "backbone_nodes"
                  ],
                  initial_states=trained_network._get_initial_decoder_states(
                      gnn_output
                  ),
                  neural_model=trained_network._decoder,
              )
              decoded_sequences.extend(mb_outputs)
  
      assert len(decoded_sequences) == len(data)
      return decoded_sequences
  
imports: from typing_extensions import Final, TypedDict
  import torch
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel, ModuleWithMetrics
  from ptgnn.baseneuralmodel.utils.data import enforce_not_None
  from ptgnn.neuralmodels.gnn.graphneuralnetwork import GraphNeuralNetwork, GraphNeuralNetworkModel
  from ptgnn.neuralmodels.gnn.structs import GnnOutput, GraphData, TensorizedGraphData
  from ptgnn.neuralmodels.reduceops import AbstractVarSizedElementReduce, ElementsToSummaryRepresentationInput, MultiheadSelfAttentionVarSizedElementReduce, SimpleVarSizedElementReduce
  from ptgnn.neuralmodels.sequence.grucopydecoder import DecoderData, GruCopyingDecoder, GruCopyingDecoderModel, TokenizedOutput
label_desc: Use image node_id 8 for calling the Graph2Seq obj's underlying member method code with example usage: obj.greedy_decode(data, trained_network, device) and returns: decoded_sequences